R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning

TL;DR

This paper introduces R2-D2, a semi-supervised learning framework that provides theoretical support for pseudo-labeling, proposes a repetitive reprediction strategy to improve pseudo-label certainty, and achieves superior results on ImageNet.

Contribution

It offers a theoretical foundation linking network predictions to pseudo-labels and introduces the R2 strategy to enhance semi-supervised learning performance.

Findings

Outperforms state-of-the-art methods by 5% on ImageNet

Provides theoretical support for using network predictions as pseudo-labels

Introduces the R2 strategy to reduce pseudo-label uncertainty

Abstract

Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels in the deep learning paradigm. In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. Within the D2 framework, we prove that pseudo-labels are related to network predictions by an exponential link function, which gives a theoretical support for using predictions as pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by network predictions will make them uncertain. To mitigate this problem, we propose a training strategy called repetitive reprediction (R2). Finally, the proposed R2-D2 method is tested on the large-scale ImageNet dataset and outperforms state-of-the-art methods by 5 percentage points.